HoneyHive vs LangWatch (2026)

More than fifty libraries are instrumented automatically, including LangChain, LangGraph, AWS Strands, Google ADK and the OpenAI Agents SDK. HoneyHive accepts traces from third party agent platforms such as ServiceNow, Microsoft Copilot Studio and Salesforce Agentforce and sends alerts by email, Slack or webhook. These bring trace data in and send notifications out, and none connects an agent to a real system to take actions.

SourceHoneyHive, honeyhive.ai/llms.txt and pricing, and docs.honeyhive.ai alertsread 2026-09-21

Langy, LangWatch's agent, changes code through pull requests that a GitHub App opens. LangWatch workflows, which can be published as agents or API endpoints, call outside systems through HTTP nodes and code blocks that use encrypted project secrets. The AI Gateway governs which tools and servers a virtual key may reach.

SourceLangWatch, langwatch.ai/docs/llms.txt (Langy pull requests, workflows, secrets, routing policies)read 2026-09-21

The customer's own agent workflows are drawn as graphs and trajectories from their traces. HoneyHive itself runs no workflows that sequence, branch or retry steps, or that combine deterministic nodes with agent steps.

SourceHoneyHive, docs.honeyhive.ai graph-view and trajectory-viewread 2026-09-21

A LangWatch workflow is a graph of nodes that runs from an Entry point to an End node, mixing LLM prompt nodes with Python code, HTTP and evaluator nodes. Versions are saved and can be restored, and a workflow can be published as an evaluator, as an agent under test, or as an API the customer's code calls. Workflows do not include loops, branching, retries or fallback paths.

SourceLangWatch, langwatch.ai/docs/workflows/overview and llms.txt (building a workflow, workflow as agent); langwatch.ai/docs/workflows/overview.mdread 2026-09-21

Online evaluations run enabled evaluators automatically and asynchronously on incoming traces that match their filters, at a set sampling rate, and alerts check cost, latency, error and evaluator-score thresholds on a regular cycle and notify by email, Slack or webhook when one is crossed. Evaluation starts on an incoming trace or a monitoring cycle, with no person asking.

SourceHoneyHive, docs.honeyhive.ai monitoring onlineevals, alerts overview and alertsread 2026-09-21

Online evaluation monitors run an evaluator on every matching trace or thread as it arrives, alerts and automations catch regressions and notify teams, automations add every new trace matching a filter to a dataset, and Langy can set up alerts and automations from what it finds across traces. Scoring and actions start on incoming traffic without anyone asking.

SourceLangWatch, langwatch.ai/docs/llms.txt (online evaluation, alerts and automations, datasets from traces, Langy insights)read 2026-09-21

Evaluator templates score the customer's own retrieval for context relevance and answer faithfulness, and traces record the context that retrieval returned. There is no index, retrieval layer or knowledge API that grounds an agent's behavior in company data.

SourceHoneyHive, docs.honeyhive.ai evaluator-templates and tracing conceptsread 2026-09-21

Built in RAG evaluators score retrieval, and cookbooks on embedding tuning and vector versus hybrid search help with the customer's own pipeline. LangWatch has no document ingestion, index, retrieval layer or knowledge API that grounds an agent in company data.

SourceLangWatch, langwatch.ai/docs/llms.txt (built-in evaluators, cookbooks)read 2026-09-21

Sessions, events and datasets persist as observability and test records of what the customer's agent did, and a context retention evaluator scores whether that agent kept information across turns. There is no session, conversation, workflow or long term memory that an agent reads and writes.

SourceHoneyHive, docs.honeyhive.ai tracing concepts and evaluator-templatesread 2026-09-21

Traces, threads and datasets are stored as records of the customer's agent, and each Langy conversation runs in its own sandboxed worker. Agents have no session, workflow or long term memory that they read and write across runs.

SourceLangWatch, langwatch.ai/docs/llms.txt (concepts, datasets, how Langy works)read 2026-09-21

Annotation queues organize events for human review against human evaluators that define the fields a team rates, and those judgments become ground truth for automated evaluators. Feedback from end users can be attached to traces. People review the agent's outputs, but there is no approval step, consent checkpoint, runtime guardrail or control to pause and resume an agent's actions.

SourceHoneyHive, docs.honeyhive.ai annotation-queues, evaluators/human and setting-user-feedbackread 2026-09-21

Guardrails run evaluators inline to block or modify harmful responses in real time, the AI Gateway enforces budgets, rate limits and routing policies, including which tools and servers a key may reach, before a request goes through, and Langy's code changes arrive as pull requests that a person reviews before merge.

SourceLangWatch, langwatch.ai/docs/llms.txt (guardrails, AI Gateway budgets and routing policies, Langy pull requests)read 2026-09-21

HoneyHive holds SOC 2 Type II, audited annually across all hosting options, with reports available through its Drata Trust Center, plus GDPR and HIPAA BAAs on Enterprise.

Users sign in with Google, GitHub or Microsoft SSO or SAML 2.0 with Okta, Entra ID, Google Workspace, OneLogin or Ping, with roles provisioned from SAML group claims and MFA for every account. Role based access applies at organization, workspace and project level, with custom roles on Enterprise.

Data is encrypted with AWS KMS at rest, with keys managed by the customer on dedicated and self hosted deployments, and with TLS 1.2+ in transit.

SourceHoneyHive, docs.honeyhive.ai setup/security and workspace rolesread 2026-09-21

Controls include single sign on with directory provisioning and sign in policies, SCIM 2.0 provisioning from Okta, Entra ID and others with group to role mapping, and role based access with custom roles and role bindings at organization, team and project level.

An audit log records every change made through settings, the API and the AI Gateway, with who, where and what changed, and exports to a SIEM in OCSF format. Retention is set per organization, team or project, and secrets are encrypted and write only. LangWatch states ISO 27001 certification and GDPR compliance, with a trust center monitored by Vanta.

SourceLangWatch, langwatch.ai/docs/llms.txt (SSO, SCIM, RBAC, audit log, data retention, secrets, OCSF export), langwatch.ai homepage and pricingread 2026-09-21

Each agent run is recorded as a session of OpenTelemetry events, covering model calls, tool calls, retrieval and custom spans, and shown in tree, timeline, graph, trajectory and thread views. Trace data can be queried through the API, every plan can export data, and retention is 30 days on Developer and custom on Enterprise. The platform is built as an audit trail, so individual records cannot be deleted through the API or UI.

SourceHoneyHive, docs.honeyhive.ai tracing concepts, tree-view, graph-view, query-data and self-hosted data-flow, and honeyhive.ai/pricingread 2026-09-21

LangWatch traces LLM calls, tool executions and retrieval steps through OpenTelemetry native SDKs and integrations, including coding assistants such as Claude Code and Codex. An audit log records every change through settings, the API and the gateway, governance events export to a SIEM as OCSF records, and analytics export into the customer's dashboards. Default retention is published per plan and can be adjusted per organization, team or project.

SourceLangWatch, langwatch.ai/docs/llms.txt (observability, audit log, OCSF export, data retention) and langwatch.ai/docs/pricingread 2026-09-21

Multi tenant SaaS runs in AWS US-West-2. Dedicated Cloud keeps the data plane on physically isolated infrastructure in the customer's chosen AWS region, including the EU, with the control plane managed by HoneyHive. A hybrid option pairs a control plane managed by HoneyHive with a self hosted data plane, and a fully self hosted option deploys both planes in the customer's own account, with PrivateLink for private connectivity.

SourceHoneyHive, docs.honeyhive.ai setup managed, dedicated, self-hosted and security, and honeyhive.ai/pricingread 2026-09-21

Customers can run LangWatch as managed multi tenant SaaS in the EU, US, UK or APAC, self hosted with Docker, Kubernetes and Helm or in their own VPC, or hybrid, with the data plane on their infrastructure and the control plane on LangWatch's. The open source repository includes every feature for local use, and an Enterprise license adds longer retention to self hosted deployments.

SourceLangWatch, langwatch.ai homepage and langwatch.ai/docs/llms.txt (self-hosting overview, editions and licensing, hybrid setup)read 2026-09-21

Three official agent skills, honeyhive-instrument, honeyhive-evaluate and honeyhive-improve, are reusable HoneyHive workflows that a coding agent follows. On Enterprise, organization templates let platform teams define blueprints of evaluators and monitoring charts that every new project inherits. These are packaged starting points for setting HoneyHive up, and teams can edit them. There are no prebuilt agents, packaged employees or deployable workflows for a customer's own work.

SourceHoneyHive, docs.honeyhive.ai ai-coding-agents and workspace/templatesread 2026-09-21

LangWatch ships Langy, an automated AI engineer that reads traces and evals, writes Scenario tests, optimizes prompts and opens pull requests, and a Skills Directory of installable skills, including skills for product managers and domain experts, that set a coding assistant up to work with LangWatch. There is one packaged agent and a set of setup skills, but no set of ready made agents or workflow templates to choose from.

SourceLangWatch, langwatch.ai/docs/llms.txt (Langy, skills directory) and langwatch.ai homepageread 2026-09-21

Workspace admins store their own provider credentials for Anthropic, OpenAI, Azure OpenAI, Amazon Bedrock, Gemini and Vertex AI, plus an OAuth gateway on self hosted deployments. HoneyHive uses them for LLM evaluators and the Playground, with provider access scoped by workspace. Custom model providers are available in prompt management on every plan, and evaluators can run through Portkey.

SourceHoneyHive, docs.honeyhive.ai workspace/provider-keys and evaluators/portkey, and honeyhive.ai/pricingread 2026-09-21

The LangWatch AI Gateway offers one endpoint, compatible with OpenAI and Anthropic, across providers including OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, xAI, Groq, Cerebras and DeepSeek. Provider credentials are held in LangWatch, each application gets a virtual key, and routing policies set which providers a key routes through, the fallback order and model tiers, with budgets and rate limits enforced before the request. Model providers are stored once per organization, team or project for evaluators, workflows and Langy.

SourceLangWatch, langwatch.ai/docs/llms.txt (AI Gateway, routing policies, budgets, providers, model providers)read 2026-09-21

Python and TypeScript SDKs built on OpenTelemetry make HoneyHive callable from code, alongside a REST API for events, sessions, datasets, experiments and evaluators, a Control Plane API for workspaces, data planes and alerts, and a CLI for terminal access to resources. Other languages send OpenTelemetry traces to its collector, and CI regression detection fits evaluation into a delivery pipeline. It also ships agent skills and an MCP docs search for coding agents.

SourceHoneyHive, docs.honeyhive.ai sdk-reference, API and control-plane references, cli-reference, ai-coding-agents and ci-regression-detectionread 2026-09-21

LangWatch offers a REST API, including the Workflows and Instant Evals APIs, SDKs in Python, TypeScript, Go and Java, and the langwatch CLI, which drives the platform from the terminal for people and coding assistants. An MCP server gives coding assistants access to traces, tests and evaluations, alongside installable skills, personal and service API keys for development and CI/CD, and CI integration that gates merges on eval results.

SourceLangWatch, langwatch.ai/docs/llms.txt (CLI, MCP server, API keys, API reference) and langwatch.ai/pricingread 2026-09-21

Experiments run an application over datasets and score it with versioned Python, LLM as a judge and human evaluators, and results are compared across runs. CI regression detection catches drops before a change ships. Online evaluations score production traces continuously, and production events can be curated into datasets for the next experiment.

SourceHoneyHive, docs.honeyhive.ai evaluation introduction, comparing evals, ci-regression-detection, onlineevals and dataset-curationread 2026-09-21

Experiments run over datasets with built in, saved and workflow built evaluators. LangWatch also tests agents with multi turn scenarios in which a simulated user acts against the agent and a judge scores the conversation. It groups scenarios into test suites, compares agents side by side on pass rate, cost and latency, gates merges on eval results in CI, scores production traffic with online monitors, and optimizes prompts and tool contracts with the scenario suite as the quality gate.

SourceLangWatch, langwatch.ai/docs/llms.txt (agent testing, evaluations, improve your agent) and langwatch.ai/pricingread 2026-09-21

The product covers tracing, monitoring, evaluation, datasets, prompts, workspace settings and deployment, and there is no browser, desktop or computer control.

SourceHoneyHive, docs.honeyhive.ai and llms-full.txtread 2026-09-21

The product covers tracing, agent testing, evaluation, the AI Gateway, governance, Langy and self hosting, and no agent controls a browser, desktop or computer. Langy works in a sandbox through the CLI and code.

SourceLangWatch, langwatch.ai/docs/llms.txtread 2026-09-21